Banks play a crucial role in market economies. They decide who can get finance and on what terms and can make or break investment decisions.

For markets and society to function, individuals and companies need access to credit. Credit scoring algorithms, which make a guess at the probability of default, are the method banks use to determine whether or not a loan should be granted. This competition requires participants to improve on the state of the art in credit scoring, by predicting the probability that somebody will experience financial distress in the next two years. The goal of this competition is to build a model that borrowers can use to help make the best financial decisions.

==>The goal of this project is to build a model that borrowers can use to help make the best financial decisions.

Historical data are provided on 250,000 borrowers.https://www.kaggle.com/c/GiveMeSomeCredit

summary: we are expecting the following:

1.Methodology: method for engineering our solution a.CRISP_DM

Model a.We are expecting a machine learning model that can correctly classify financial decisions.

Final Result :

Complete pipe: preproceess+Modeling We aim to predict Credit scoring algorithms, which make a guess at the probability of default, are the method banks use to determine whether or not a loan should be granted. We will evaluate model performance with the:

F beta score

ROC AUC score

PR AUC score | Average precision

Final Evaluation :

-Run 4308.6s

-Private Score 0.86914 ==>top5 solution

-Public Score 0.86245

take look at kaggle for verification :

https://www.kaggle.com/bannourchaker/credit-part4-evaluation-all/notebook?scriptVersionId=84977736

https://www.kaggle.com/bannourchaker/credit-part1-dataunderstanding?scriptVersionId=84915808